What You See Ain't Necessarily What You Got
Int J Radiat Oncol Biol Phys. 2024 Apr 1;118(5):1164-1166. doi: 10.1016/j.ijrobp.2023.09.004. Epub 2024 Mar 14.NO ABSTRACTPMID:38492967 | DOI:10.1016/j.ijrobp.2023.09.004 (Source: Physics in Medicine and Biology)
Source: Physics in Medicine and Biology - March 16, 2024 Category: Physics Authors: Lawrence B Marks Shiva K Das Joel E Tepper Source Type: research

What You See Ain't Necessarily What You Got
Int J Radiat Oncol Biol Phys. 2024 Apr 1;118(5):1164-1166. doi: 10.1016/j.ijrobp.2023.09.004. Epub 2024 Mar 14.NO ABSTRACTPMID:38492967 | DOI:10.1016/j.ijrobp.2023.09.004 (Source: Physics in Medicine and Biology)
Source: Physics in Medicine and Biology - March 16, 2024 Category: Physics Authors: Lawrence B Marks Shiva K Das Joel E Tepper Source Type: research

Evaluation of monolithic crystal detector with dual-ended readout utilizing multiplexing method
In this study, we constructed and evaluated a dual-ended readout monolithic crystal detector based on a multiplexing method.APPROACH: We employed two 12×12 silicon photomultiplier (SiPM) arrays for readout, and the signals from the 12 × 12 array were merged into 12 X and 12 Y channels using channel multiplexing. In 2D reconstruction, three methods based on the centre of gravity (COG) were compared, and the concept of thresholds was introduced. Furthermore, a light convolutional neural network (CNN)
was employed for testing. To enhance depth localization resolution, we proposed a method by utilizing the mutual inf...
Source: Physics in Medicine and Biology - March 14, 2024 Category: Physics Authors: Xiangtao ZengXiangtao Zhiming Zhang Daowu Li Xianchao Huang Zhuoran Wang Yingjie Wang Wei Zhou Peilin Wang Meiling Zhu Qing Wei Huixing Gong Long Wei Source Type: research

Aleatoric and epistemic uncertainty extraction of patient-specific deep learning-based dose predictions in LDR prostate brachytherapy
This study aims to establish fast DL-based predictive dose algorithms for LDR (low-dose rate) prostate brachytherapy and to evaluate their uncertainty and stability.

Approach: Data from 200 prostate patients, treated with125I sources, was collected. The Monte Carlo (MC) ground truth dose volumes were calculated with TOPAS considering the interseed effects and an organ-based material assignment. Two 3D convolutional neural networks, UNet and ResUNet TSE, were trained using the patient geometry and the seed positions as the input. The dataset was randomly split into training (150), validation (25) and test (...
Source: Physics in Medicine and Biology - March 14, 2024 Category: Physics Authors: Francisco Berumen Samuel Ouellet Shirin A Enger Luc Beaulieu Source Type: research

2.5D UNet with context-aware feature sequence fusion for accurate esophageal tumor semantic segmentation
Phys Med Biol. 2024 Mar 14. doi: 10.1088/1361-6560/ad3419. Online ahead of print.ABSTRACTSegmenting esophageal tumor from Computed Tomography (CT) sequence images can assist doctors in diagnosing and treating patients with this malignancy. However, accurately extracting esophageal tumor features from CT images often present challenges due to their small area, variable position, and shape, as well as the low contrast with surrounding tissues. This results in not achieving the level of accuracy required for practical applications in current methods. To address this problem, we propose a 2.5D Context-Aware Feature Sequence Fu...
Source: Physics in Medicine and Biology - March 14, 2024 Category: Physics Authors: Kai Xu Feixiang Zhanga Yong Huang Xiaoyu Huang Source Type: research

Cross noise level PET denoising with continuous adversarial domain generalization
Phys Med Biol. 2024 Mar 14. doi: 10.1088/1361-6560/ad341a. Online ahead of print.ABSTRACTObjective
Performing PET denoising within the image space proves effective in reducing the variance in PET images. In recent years, deep learning has demonstrated superior denoising performance, but models trained on a specific noise level typically fail to generalize well on different noise levels, due to inherent distribution shifts between inputs. The distribution shift usually results in bias in the denoised images. Our goal is to tackle such a problem using a domain generalization technique.
Approach
We pro...
Source: Physics in Medicine and Biology - March 14, 2024 Category: Physics Authors: Xiaofeng Liu Samira Vafay Eslahi Thibault Marin Amal Tiss Yanis Chemli Yongsong Huang Keith Johnson Georges El Fakhri Jinsong Ouyang Source Type: research

Evaluation of monolithic crystal detector with dual-ended readout utilizing multiplexing method
In this study, we constructed and evaluated a dual-ended readout monolithic crystal detector based on a multiplexing method.APPROACH: We employed two 12×12 silicon photomultiplier (SiPM) arrays for readout, and the signals from the 12 × 12 array were merged into 12 X and 12 Y channels using channel multiplexing. In 2D reconstruction, three methods based on the centre of gravity (COG) were compared, and the concept of thresholds was introduced. Furthermore, a light convolutional neural network (CNN)
was employed for testing. To enhance depth localization resolution, we proposed a method by utilizing the mutual inf...
Source: Physics in Medicine and Biology - March 14, 2024 Category: Physics Authors: Xiangtao ZengXiangtao Zhiming Zhang Daowu Li Xianchao Huang Zhuoran Wang Yingjie Wang Wei Zhou Peilin Wang Meiling Zhu Qing Wei Huixing Gong Long Wei Source Type: research

Aleatoric and epistemic uncertainty extraction of patient-specific deep learning-based dose predictions in LDR prostate brachytherapy
This study aims to establish fast DL-based predictive dose algorithms for LDR (low-dose rate) prostate brachytherapy and to evaluate their uncertainty and stability.

Approach: Data from 200 prostate patients, treated with125I sources, was collected. The Monte Carlo (MC) ground truth dose volumes were calculated with TOPAS considering the interseed effects and an organ-based material assignment. Two 3D convolutional neural networks, UNet and ResUNet TSE, were trained using the patient geometry and the seed positions as the input. The dataset was randomly split into training (150), validation (25) and test (...
Source: Physics in Medicine and Biology - March 14, 2024 Category: Physics Authors: Francisco Berumen Samuel Ouellet Shirin A Enger Luc Beaulieu Source Type: research

2.5D UNet with context-aware feature sequence fusion for accurate esophageal tumor semantic segmentation
Phys Med Biol. 2024 Mar 14. doi: 10.1088/1361-6560/ad3419. Online ahead of print.ABSTRACTSegmenting esophageal tumor from Computed Tomography (CT) sequence images can assist doctors in diagnosing and treating patients with this malignancy. However, accurately extracting esophageal tumor features from CT images often present challenges due to their small area, variable position, and shape, as well as the low contrast with surrounding tissues. This results in not achieving the level of accuracy required for practical applications in current methods. To address this problem, we propose a 2.5D Context-Aware Feature Sequence Fu...
Source: Physics in Medicine and Biology - March 14, 2024 Category: Physics Authors: Kai Xu Feixiang Zhanga Yong Huang Xiaoyu Huang Source Type: research

Cross noise level PET denoising with continuous adversarial domain generalization
Phys Med Biol. 2024 Mar 14. doi: 10.1088/1361-6560/ad341a. Online ahead of print.ABSTRACTObjective
Performing PET denoising within the image space proves effective in reducing the variance in PET images. In recent years, deep learning has demonstrated superior denoising performance, but models trained on a specific noise level typically fail to generalize well on different noise levels, due to inherent distribution shifts between inputs. The distribution shift usually results in bias in the denoised images. Our goal is to tackle such a problem using a domain generalization technique.
Approach
We pro...
Source: Physics in Medicine and Biology - March 14, 2024 Category: Physics Authors: Xiaofeng Liu Samira Vafay Eslahi Thibault Marin Amal Tiss Yanis Chemli Yongsong Huang Keith Johnson Georges El Fakhri Jinsong Ouyang Source Type: research

Development of a novel fibre optic beam profile and dose monitor for very high energy electron radiotherapy at ultrahigh dose rates
This study introduces the Fibre Optic FLASH Monitor (FOFM), which consists of an array of silica optical fibre-based Cherenkov sensors with a photodetector for signal readout.
Approach: Experiments were conducted at the CLEAR facility at CERN using 200 MeV and 160 MeV electrons to assess the FOFM's response linearity to UHDR (characterised with radiochromic films) required for FLASH radiotherapy. Beam profile measurements made on the FOFM were compared to those using radiochromic film and scintillating Yttrium Aluminium Garnet (YAG) screens.
Main Results: A range of photodetectors were evaluated, with a Com...
Source: Physics in Medicine and Biology - March 13, 2024 Category: Physics Authors: Joseph John Bateman Emma Buchanan Roberto Corsini Wilfrid Farabolini Pierre Korysko Robert Garbrecht Larsen Alexander Malyzhenkov I ñaki Ortega Ruiz Vilde Rieker Alexander Gerbershagen Manjit Dosanjh Source Type: research

3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR)
Phys Med Biol. 2024 Mar 13. doi: 10.1088/1361-6560/ad33b7. Online ahead of print.ABSTRACT3D cine-magnetic resonance imaging (cine-MRI) can capture images of the human body volume with high spatial and temporal resolutions to study the anatomical dynamics. However, the reconstruction of 3D cine-MRI is challenged by highly under-sampled k-space data in each dynamic (cine) frame, due to the slow speed of MR signal acquisition. We proposed a machine learning-based framework, spatial and temporal implicit neural representation learning (STINR-MR), for accurate 3D cine-MRI reconstruction from highly under-sampled data.
A...
Source: Physics in Medicine and Biology - March 13, 2024 Category: Physics Authors: Hua-Chieh Shao Tielige Mengke Jie Deng You Zhang Source Type: research

Verification of neuronavigated TMS accuracy using structured-light 3D scans
Phys Med Biol. 2024 Mar 13. doi: 10.1088/1361-6560/ad33b8. Online ahead of print.ABSTRACTObjective
To investigate the reliability and accuracy of the manual three-point co-registration in neuronavigated transcranial magnetic stimulation (TMS). The effect of the error in landmark pointing on the coil placement and on the induced electric and magnetic fields was examined.

Approach
The position of the TMS coil on the head was recorded by the neuronavigation system and by 3D scanning for ten healthy participants. The differences in the coil locations and orientations and the theoretical error v...
Source: Physics in Medicine and Biology - March 13, 2024 Category: Physics Authors: Noora Matilainen Juhani Kataja Ilkka Laakso Source Type: research

3D whole heart k-space-based super-resolution cardiac T1 mapping using rotated stacks
Phys Med Biol. 2024 Mar 13. doi: 10.1088/1361-6560/ad33b6. Online ahead of print.ABSTRACTTo provide 3D whole-heart high-resolution isotropic cardiac T1 maps using a k-space-based through-plane super-resolution reconstruction (SRR) with rotated multi-slice stacks.

Approach: Due to limited SNR and cardiac motion, often only 2D T1 maps with low through-plane resolution (4-8 mm) can be obtained. Previous approaches used SRR to calculate 3D high-resolution isotropic cardiac T1 maps. However, they were limited to the ventricles. The proposed approach acquires rotated stacks in long-axis orientation with high in-...
Source: Physics in Medicine and Biology - March 13, 2024 Category: Physics Authors: Simone Hufnagel Patrick Schuenke Jeanette Schulz-Menger Tobias Schaeffter Christoph Kolbitsch Source Type: research

IWNeXt: an image-wavelet domain ConvNeXt-based network for self-supervised multi-contrast MRI reconstruction
Phys Med Biol. 2024 Mar 13. doi: 10.1088/1361-6560/ad33b4. Online ahead of print.ABSTRACTOBJECTIVE: Multi-contrast magnetic resonance imaging (MC MRI) can obtain more comprehensive anatomical information of the same scanning object but requires a longer acquisition time than single-contrast MRI. To accelerate MC MRI speed, recent studies only collect partial k-space data of one modality (target contrast) to reconstruct the remaining non-sampled measurements using a deep learning-based model with the assistance of another fully sampled modality (reference contrast). However, MC MRI reconstruction mainly performs the image d...
Source: Physics in Medicine and Biology - March 13, 2024 Category: Physics Authors: YangHui Yan TieJun Yang ChunXia Jiao AoLin Yang JianYu Miao Source Type: research